Abstract

Abstract Summary: We describe two new Generalized Hidden Markov Model implementations for ab initio eukaryotic gene prediction. The C/C++ source code for both is available as open source and is highly reusable due to their modular and extensible architectures. Unlike most of the currently available gene-finders, the programs are re-trainable by the end user. They are also re-configurable and include several types of probabilistic submodels which can be independently combined, such as Maximal Dependence Decomposition trees and interpolated Markov models. Both programs have been used at TIGR for the annotation of the Aspergillus fumigatus and Toxoplasma gondii genomes. Availability: Source code and documentation are available under the open source Artistic License from http://www.tigr.org/software/pirate.

Keywords

Source codeComputer scienceMIT LicenseOpen sourceSoftwareProbabilistic logicDocumentationModular designProgramming languageAnnotationTheoretical computer scienceHidden Markov modelLicenseCode (set theory)Computational biologyArtificial intelligenceBiologySet (abstract data type)Operating system

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Publication Info

Year
2004
Type
article
Volume
20
Issue
16
Pages
2878-2879
Citations
1892
Access
Closed

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Cite This

William H. Majoros, Mihaela Pertea, Steven L. Salzberg (2004). TigrScan and GlimmerHMM: two open source <i>ab initio</i> eukaryotic gene-finders. Bioinformatics , 20 (16) , 2878-2879. https://doi.org/10.1093/bioinformatics/bth315

Identifiers

DOI
10.1093/bioinformatics/bth315